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Found 7 papers, 4 papers with code
On the Content Bias in Fréchet Video Distance
We show that FVD with features extracted from the recent large-scale self-supervised video models is less biased toward image quality.
One-Step Image Translation with Text-to-Image Models
In this work, we address two limitations of existing conditional diffusion models: their slow inference speed due to the iterative denoising process and their reliance on paired data for model fine-tuning.
Zero-shot Image-to-Image Translation
However, it is still challenging to directly apply these models for editing real images for two reasons.
Ranked #13 on
Text-based Image Editing
on PIE-Bench
Spatially-Adaptive Multilayer Selection for GAN Inversion and Editing
We propose a new method to invert and edit such complex images in the latent space of GANs, such as StyleGAN2.
On Aliased Resizing and Surprising Subtleties in GAN Evaluation
Furthermore, we show that if compression is used on real training images, FID can actually improve if the generated images are also subsequently compressed.
Dual Contradistinctive Generative Autoencoder
Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for there construction/synthesis), both being contradistinctive.
Ranked #2 on
Image Generation
on LSUN Bedroom 128 x 128
Guided Variational Autoencoder for Disentanglement Learning
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning.
